For second set of benchmark, we selected 2 popular embedded deep learning architecture MobilNets and YOLO for performance analysis.
MobileNet [1]} is a CNN architecture designed for mobile and embedded based vision applications where there is a lack of computing power. It provides high accuracy while the size of the network is relatively small. This architecture is based on depthwise separable convolutions, making it proper for building small models that can be matched to mobile and embedded vision applications.
YOLO is a real-time object detection architecture designed for real-time processing. It uses a multi-scale training method to detect objects. It divides the image into a grid with bounding boxes, which are drawn around images. Predicted probabilities for each region are then calculated based on the weights that are associated with the probabilities[2]}.
